Exploration of Vehicle Target Detection and Classification Method Based on Sea Lion Optimization with Deep Convolutional Neural Network

Abstract

Abstract Presently, urban environments over the globe are highly employed to obtain solutions for enhancing the quality of the livers and enhance the usage of city infrastructure and resources with minimal operational cost. Urban remote sensing acts as a significant part in the ability of mapping, monitoring, and controlling infrastructure. High-resolution remote sensing data renders worldwide images faster than traditional data collection strategies. Hence, small objects such as cars are easily detected. Vehicle recognition on aerial remote sensing images (RSIs) in the complicated background of urban zones has always gained a lot of interest in the remote sensing field. The automatic vehicles enumeration research domain had a significant contribution in several applications, including traffic management and monitoring. Target detection technology will be a crucial part of computer vision (CV) technology, and target detection techniques were enforced in several domains. Therefore, this study develops a new Vehicle Recognition and Classification using Sea Lion Optimization with Deep Learning (VRC-SLODL) model on RSI. In the presented VRC-SLODL technique, the major intention lies in recognising and classifying vehicles present in the images. The bilateral filtering (BF) technique can initially improve the RSI quality. The VRC-SLODL technique employs a modified residual network (ResNet) model to produce a collection of feature vectors. Eventually, the SLO approach with long short-term memory (LSTM) technique was exploited for vehicle classification, where the SLO algorithm acts as a hyperparameter optimizer. The experiments were performed on a benchmark dataset to examine the better performance of the VRC-SLODL method. The obtained values reported the improved classification performance of the VRC-SLODL technique over other models.